Deep learning meets metabolomics: a methodological perspective. Issue 2 (17th September 2020)

• Record Type: Journal Article
• Authors: Sen, Partho; Lamichhane, Santosh; Mathema, Vivek B; McGlinchey, Aidan; Dickens, Alex M; Khoomrung, Sakda; Orešič, Matej
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 2(2021)
• Page Start: 1531
• Page End: 1542
• Publication Date: 2020-09-17
• Subjects: artificial intelligence -- genome-scale metabolic modelling -- lipidomics -- machine learning -- deep learning -- metabolism -- metabolomics; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa204
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.